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VantageScore 4.0:
Rise of the Machine… Learning

It’s been the Catch 22 of credit: Consumers who aren’t continually active users of credit struggle to get approved for new credit accounts.

For years, consumers who didn’t have a recent track record of credit cards, loans or other types of borrowing were either frozen out of getting a loan altogether, forced to pay higher interest rates or required to turn to other alternatives, such as pawn shops or payday lenders.

VantageScore 3.0, the most recent commercially available VantageScore model, scores up to 30-35 million consumers who can’t obtain a credit score under the older, conventional scoring models used by many financial institutions, including all mortgage lenders. The recently announced VantageScore 4.0 model, which will be commercially available this fall, applies the latest in computer-driven, machine-learning technology to drive additional predictive performance within this population of consumers.

Specifically, VantageScore 4.0 leverages this technique to generate a more predictive credit score among those who have not engaged in credit activity in the past six months, but have relevant information in their credit file that is more than six months old.

Data scientists at VantageScore used machine learning to take an even deeper dive into the credit histories of these consumers who are often overlooked by lenders. They examined a large number of specific details within credit reports to identify additional patterns that correspond with good financial behavior or increased risk.

“These consumers may not be high-frequency users of credit; and more importantly, have not engaged in credit activity in the last six months,” explains Nick Rose, one of the principal scientists for VantageScore. “Their credit use tends to be sporadic. For example, they might open an account for a few months, and then not use it again. If you try to score them with the conventional credit-scoring techniques, you can’t get any meaningful insight based on their credit usage and patterns.”

Indeed, although it’s relatively easy to predict the creditworthiness of a borrower who has had a mortgage for several years, as well as car loans, credit cards or other installment debt, analyzing and predicting the credit behavior of consumers with less recent credit histories called for a more technical solution.

“Machine learning found some 50,000 different relationships worth considering,” said Rose. “We took those 50,000 and reduced it to 300 potential relationships we could focus on to determine which ones were the most valuable and predictive,” Rose says.

For example, the machine learning approach helped data scientists assign default rate profiles based on the relationship between the balances on collection accounts, the ages of the collection accounts and the numbers of inquiries that those consumers had recently made.

Importantly, attributes that were discovered with this approach also adhere to certain standards for inclusion into a generic credit scoring model, such as ability to translate the attributes into predictive reason codes.

“The use of machine learning provided a significant performance boost within a key population,” said Rose. “And equally important is that we were able to incorporate our findings into the confines of a regulatory compliant, generic credit scoring model that lenders can implement into their current systems.”

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